22 papers · ranked by Valyu relevance
Abhijit Chakraborty, Suddhasvatta Das, Kevin Gary
- Machine learning and AI have been recently embraced by many companies. Machine Learning Operations, (MLOps), refers to the use of continuous software engineering processes, such as DevOps, in the deployment of machine learning models to production. Nevertheless, not all machine learning initiatives successfully…
Mohammad Heydari, Zahra Rezvani
— Data is becoming more complex, and so are the approaches designed to process it. Enterprises have access to more data than ever, but many still struggle to glean the full potential of insights from what they have. This research explores the challenges and experiences of Iranian developers in implementing the MLOps…
Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl
The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations. The paradigm of Machine Learning Operations…
Sergio Moreschi, Gilberto Recupito, Valentina Lenarduzzi, Fabio Palomba + 2 more
'Fabio Palomba' 'David Hästbacka' 'Davide Taibi'] MLOps tools enable continuous development of machine learning, following the DevOps process. Different MLOps tools have been presented on the market, however, such a number of tools often create confusion on the most appropriate tool to be used in each DevOps phase. To…
Beyza Eken, Samodha Pallewatta, Nguyen Khoi Tran, Ayse Tosun + 1 more
'Muhammad Ali Babar'] With the increasing trend of Machine Learning (ML) enabled software applications, the paradigm of ML Operations (MLOps) has gained tremendous attention of researchers and practitioners. MLOps encompasses the practices and technologies for streamlining the resources and monitoring needs of…
Raúl Miñón, Josu Diaz-de-Arcaya, Ana I. Torre-Bastida, Philipp Hartlieb + 1 more
Development and operations (DevOps), artificial intelligence (AI), big data and edge-fog-cloud are disruptive technologies that may produce a radical transformation of the industry. Nevertheless, there are still major challenges to efficiently applying them in order to optimise productivity. Some of them are addressed…
Nipuni Tharushika Hewage, Dulani Meedeniya
Machine Learning (ML) has become a fast-growing, trending approach in solution development in practice. Deep Learning (DL) which is a subset of ML, learns using deep neural networks to simulate the human brain. It trains machines to learn techniques and processes individually using computer algorithms, which is also…
Samar Wazir, Gautam Siddharth Kashyap, Parag Saxena
—Recently, Machine Learning (ML) has become a widely accepted method for significant progress that is rapidly evolving. Since it employs computational methods to teach machines and produce acceptable answers. The significance of the Machine Learning Operations (MLOps) methods, which can provide acceptable answers for…
José Guilherme de Almeida, Christina Messiou, Sam J. Withey, Celso Matos + 2 more
The integration of machine-learning technologies into radiology practice has the potential to significantly enhance diagnostic workflows and patient care. However, the successful deployment and maintenance of medical machine-learning (MedML) systems in radiology requires robust operational frameworks. Medical…
Yutong Li, Julie Tian, Ariana Xu, Russell Greiner + 4 more
Background The exponential growth of publications regarding the application of machine learning (ML) tools in medicine highlights the significant potential for ML to revolutionize the field. Despite the multitude of literature surrounding this topic, there are limited publications addressing the implementation and…
Saverio Ieva, Davide Loconte, Giuseppe Loseto, Federico Lopomo + 5 more
Smart buildings require intelligent and scalable solutions to monitor environmental conditions and manage increasingly complex data streams generated by distributed sensing infrastructures. In this context, the paper presents an edge-enabled Digital Twin framework for smart office environments, integrating real-time…
Mahdieh Shabanian, Nima Pouladi, Liam S. Wilson, Mattia A. Prosperi + 1 more
Ninety percent of the 65,000 human diseases are infrequent, collectively affecting ∼ 400 million people, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of…
Viacheslav Moskalenko, Vyacheslav Kharchenko
1.1### Motivation The advent of Artificial Intelligence (AI) in healthcare has opened new horizons in medical diagnostics, offering more precise, efficient, and rapid techniques for detecting a wide range of diseases. However, the critical nature of healthcare imposes strict requirements on AI-based diagnostic systems…
Fernando Orti, María Laura Fernández, Cristina Marino-Buslje
Over the past few years, there has been a focus on proteins that create separate liquid phases in the intracellular liquid environment, known as membraneless organelles (MLO). These organelles allow for the spatiotemporal associations of macromolecules that dynamically exchange within the cellular milieu. They provide…
Shael Brown, Bowei Xiao, Kathleen Oros Klein, Jośee Dupuis + 2 more
Multiomic datasets contain complex nonlinear relationships that are often missed by conventional analysis methods. Topological data analysis (TDA) can detect some such patterns (i.e., loops), and here we extend previous TDA approaches with MOLA (MultiOmic Loop Analysis), a framework for multiomic loop visualization…
Jochen Sieg, Christian Wolfgang Feldmann, Jennifer Hemmerich, Conrad Stork + 3 more
The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the MolPipeline package, which extends this concept to chemoinformatics by…
Yi Luo, Saientan Bag, Orysia Zaremba, Jacopo Andreo + 3 more
Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis…
Authors not listed
Metal-organic framework (MOF) derived materials, formed through high temperature processes, show great potential as catalysts. However, knowledge of the structure-property relationships between the initial MOF and final MOF-derived catalyst is limited, as their amorphous nature challenges standard structural…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Etinosa Osaro, Fernando Fajardo-Rojas, Gregory Cooper, Diego Gómez-Gualdrón + 1 more
Adsorption is a fundamental process studied in materials science and engineering because it plays a critical role in various applications, including gas storage and separation. Understanding and predicting gas adsorption within porous materials demands comprehensive computational simulations that are often resource…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Theo Knijnenburg, Gunnar Klau, Francesco Iorio, Mathew Garnett + 3 more
Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. Finding ‘actionable knowledge’ is becoming more important, but also more challenging as datasets grow in size and complexity. We…